Comments (5)
If you have only 1 initial face, you need to have latent directions for different expressions in order to perform:
- expression transfer.
If you have at least 2 initial faces, you could perform:
- morphing,
- style transfer.
To edit latent vectors of projected images, run stylegan2_editing_latent_vectors.ipynb
You can also check expression transfer done with encoder4editing
or other methods.
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Thanks for the answer but sorry for not being clear enough. I am using your script project_images.py
but can't find my way around changing the initial face used for projections. How can I do this?
from stylegan2-projecting-images.
This script is only used to project the initial face, i.e. to find the latent vector which would encode the face.
To change the face, you need to edit the latent vector, which I do in some of the cells of the notebook linked above.
This is also done a bit by encoder4editing
linked above, which is a nice project which did not exist when I created this repository.
from stylegan2-projecting-images.
so the initial face (not the one I input) is created by a random seed. where can I edit this noise vector in the project_images.py
script?
from stylegan2-projecting-images.
What you call the "initial face" must be the first face that you see during the projection. This is actually the "average" face.
It is initialized here in the official code by Nvidia:
https://github.com/NVlabs/stylegan2/blob/81ae61d7753c6a7b5a38c29aa81aed15adb19c2f/projector.py#L67
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Related Issues (12)
- Initial Update HOT 1
- issue in project images.py HOT 3
- faceshifter HOT 1
- Use latent vector HOT 1
- Display the landmarks HOT 2
- python project_images.py datasets/aligned_images/ results/generated_images_tiled/ --video True --video-mode 2 HOT 3
- --num-steps value HOT 2
- style transfer HOT 2
- Use(load) the npy of the first picture to continue training the npy of the second picture HOT 2
- Expression transfer -- angle_horizontal HOT 2
- Stylegan2-ada HOT 3
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